Background: Coronavirus disease (COVID-19) is an infectious disease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world. Aim: The aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia and healthy cases using deep learning techniques. Method: In this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance different CNN architectures. Results: Evaluation results using K-fold (10) showed that we have achieved state of the art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole. Conclusion: Quantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models develop in this study could be used early screening of coronavirus, however it calls for extensive need to CT or X-rays dataset to develop a reliable application.

Psoriasis, a chronic inflammatory skin disease, poses significant diagnostic challenges due to its heterogeneous clinical manifestations and overlap with other dermatological conditions. The inconsistencies in diagnostic standards and variability in disease presentation further exacerbate the controversy surrounding the diagnosis of psoriasis. Recent advances in artificial intelligence (AI), particularly deep learning (DL) techniques such as convolutional neural networks (CNNs) and transformers, have shown promising results in improving recognition of psoriasis. This comprehensive review aims to provide an in-depth examination of the current state of AI applications in the diagnosis of psoriasis, highlighting the strengths and limitations of existing approaches. We evaluate the performance of various AI models on publicly available datasets, including the Psoriasis Image Dataset (PID) and the International Skin Imaging Collaboration (ISIC) dataset, and compare their accuracy, sensitivity, and specificity with traditional diagnostic methods. Furthermore, we discuss the challenges and future directions of AI-powered psoriasis diagnosis, including the need for large, diverse datasets, addressing data quality and generalization issues, and developing explainable AI models. Our review highlights the potential of AI to revolutionize the diagnosis and management of psoriasis and provides a pathway for future research in this field.

Clinicians decisions are becoming more and more evidence-based meaning in no other field the big data analytics so promising as in healthcare. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention and helps us to improve the quality of life. Researchers and clinicians are working to inhibit big data from having a positive impact on health in the future. Different tools and techniques are being used to analyze, process, accumulate, assimilate and manage large amount of healthcare data either in structured or unstructured form. In this paper, we would like to address the need of big data analytics in healthcare: why and how can it help to improve life?. We present the emerging landscape of big data and analytical techniques in the five sub-disciplines of healthcare i.e.medical image analysis and imaging informatics, bioinformatics, clinical informatics, public health informatics and medical signal analytics. We presents different architectures, advantages and repositories of each discipline that draws an integrated depiction of how distinct healthcare activities are accomplished in the pipeline to facilitate individual patients from multiple perspectives. Finally the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare.